Thermal analysis for radiative flow of Darcy–Forchheimer nanomaterials subject to entropy generation

Abstract

Abstract Background and objective Flow due to the Darcy–Forchheimer medium is an important perspective in various geophysics, industrial processes, geothermal energy, and thermodynamic processes. The importance of the Darcy–Forchheimer medium is noticed in technical, mechanical, industrial, and scientific fields including atomic waste archive, artificial dialysis, catalytic converters, gas turbine, improved oil recuperation, atherosclerosis, grain stockpiling, geo-energy production, and warm protection designing, etc. In view of such industrial and geothermal applications, the objective of this paper is to highlight the influence of entropy generation in chemical reactive MHD (magnetohydrodynamic) Darcy–Forchheimer nanoliquid flow with radiation. Flow by an exponentially stretching permeable sheet is taken. Thermal radiation, heat source, magnetic force, and dissipation impacts are considered in thermal expression. Additionally, Buongiorno’s model with random and thermophoresis diffusions is explained. Physical features of entropy are deliberated. The first-order isothermal reaction is discussed. Methodology Non-linear expressions are reduced to the dimensionless non-linear system through the implementation of non-similar transformations. The resultant non-linear systems are solved subject to local non-similarity via the ND-solve technique Results Graphical results for entropy rate, concentration, velocity, and thermal field versus emerging variables are studied. The reverse trend holds for entropy and velocity through the magnetic variable. A larger approximation of the Eckert number intensifies the thermal field. Conclusions A higher Forchheimer number reduces the fluid flow. A reverse impact for concentration and thermal field is seen through random motion variable. Similar behavior for thermal distribution is seen by thermophoresis and radiation effects. A larger porosity variable declines the entropy rate, while the reverse effect holds for the Brinkman number. A larger diffusion variable increases the entropy generation.